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Open AccessArticle

Reduced-Complexity Artificial Neural Network Equalization for Ultra-High-Spectral-Efficient Optical Fast-OFDM Signals

Computer Engineering Department, Fahad Bin Sultan University, Tabuk 71454, Saudi Arabia
Appl. Sci. 2019, 9(19), 4038; https://doi.org/10.3390/app9194038
Received: 30 July 2019 / Revised: 14 September 2019 / Accepted: 16 September 2019 / Published: 27 September 2019
(This article belongs to the Special Issue Optics for AI and AI for Optics)
Digital-based artificial neural network (ANN) machine learning is harnessed to reduce fiber nonlinearities, for the first time in ultra-spectrally-efficient optical fast orthogonal frequency division multiplexed (Fast-OFDM) signals. The proposed ANN design is of low computational load and is compared to the benchmark inverse Volterra-series transfer function (IVSTF)-based nonlinearity compensator. The two aforementioned schemes are compared for long-haul single-mode-fiber-based links at 9.69 Gb/s direct-detected optical Fast-OFDM signals. It is shown that an 80 km extension in transmission-reach is feasible when using ANN compared to IVSTF. This occurs because ANN can tackle stochastic nonlinear impairments, such as parametric noise amplification. Using ANN, the dynamic parameters requirements of the sub-ranging quantizers can also be relaxed compared to linear equalization, such as the reduction of the optimum clipping ratio and quantization bits by 2 dB and 2-bits, respectively, and by 2 dB and 2 bits when compared to the IVTSF equalizer. View Full-Text
Keywords: optical Fast-OFDM; neural networks; nonlinearity compensation; optical fiber communications optical Fast-OFDM; neural networks; nonlinearity compensation; optical fiber communications
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A. Jarajreh, M. Reduced-Complexity Artificial Neural Network Equalization for Ultra-High-Spectral-Efficient Optical Fast-OFDM Signals. Appl. Sci. 2019, 9, 4038.

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